Department of Biomathematics, University of California, Los Angeles, Los Angeles, California.
Department of Biomathematics, University of California, Los Angeles, Los Angeles, California; Department of Mathematics, California State University, Northridge, California.
Biophys J. 2018 Jun 19;114(12):2974-2985. doi: 10.1016/j.bpj.2018.05.005.
Many biological assays are employed in virology to quantify parameters of interest. Two such classes of assays, virus quantification assays (VQAs) and infectivity assays (IAs), aim to estimate the number of viruses present in a solution and the ability of a viral strain to successfully infect a host cell, respectively. VQAs operate at extremely dilute concentrations, and results can be subject to stochastic variability in virus-cell interactions. At the other extreme, high viral-particle concentrations are used in IAs, resulting in large numbers of viruses infecting each cell, enough for measurable change in total transcription activity. Furthermore, host cells can be infected at any concentration regime by multiple particles, resulting in a statistical multiplicity of infection and yielding potentially significant variability in the assay signal and parameter estimates. We develop probabilistic models for statistical multiplicity of infection at low and high viral-particle-concentration limits and apply them to the plaque (VQA), endpoint dilution (VQA), and luciferase reporter (IA) assays. A web-based tool implementing our models and analysis is also developed and presented. We test our proposed new methods for inferring experimental parameters from data using numerical simulations and show improvement on existing procedures in all limits.
许多生物学检测方法被应用于病毒学中，以量化感兴趣的参数。两种这样的检测方法，病毒定量检测（VQA）和感染性检测（IA），分别旨在估计溶液中存在的病毒数量和病毒株成功感染宿主细胞的能力。VQA 在极其稀释的浓度下操作，结果可能受到病毒-细胞相互作用的随机变化的影响。在另一个极端，IA 中使用高病毒粒子浓度，导致大量病毒感染每个细胞，足以产生总转录活性的可测量变化。此外，宿主细胞可以通过多个颗粒在任何浓度范围内被感染，导致统计感染倍数，并在检测信号和参数估计中产生潜在的显著变异性。我们为低病毒粒子浓度和高病毒粒子浓度极限下的统计感染倍数开发了概率模型，并将其应用于噬菌斑（VQA）、终点稀释（VQA）和荧光素酶报告基因（IA）检测。还开发并呈现了一个实现我们模型和分析的基于网络的工具。我们使用数值模拟测试了从数据中推断实验参数的建议新方法，并在所有极限下显示了对现有方法的改进。
